Education

Increasing High School Graduation Rates: Early Warning Systems

Roughly one in five students in the U.S. – nearly 700,000 students each year – do not complete high school on time. To help more students graduate on time, school districts across the country use intervention programs to help struggling students get back on track academically. Yet in order to best apply those programs, schools need to identify off-track students as early as possible and enroll them in the most appropriate intervention. Increasingly, forward-looking school districts are exploring data-driven “early warning systems” that can help schools find students in need of extra support. These current identification systems are hyper-specific to each district and rely mostly on anecdotal evidence or teacher intuition rather than data-driven tools, which means that resources are not allocated optimally.

By working with diverse school districts across the country, DSaPP is using modern data science and machine learning methods to create a robust set of predictive models that improve upon current identification systems in three main ways:

Allowing school districts to prioritize interventions: Our approach outputs a ranking of students, giving educators the ability to maximize their limited resources by targeting the students most at risk of dropping out first.

Scaling across multiple school districts: Our open source models are deployable across multiple school systems with limited maintenance needs, meaning school districts do not need to start from scratch to build an in-house system.

Approaching the problem flexibly and adaptively: Our models can accept and integrate a wide range of data easily. Additionally, they can update as cohorts graduate to capture changing trends.

School districts and non-profit partners can use the informational outputs from these models to build internal dashboards that accurately assess each student’s needs in order to increase their chances of graduating on time.

Predicting College Persistence and Targeting Support

Low-income students disproportionately leave college before completing a degree. DSaPP is building a model to predict which students are most at risk of not persisting through college to help schools ensure that their students and alumni receive the support they need in order to graduate.

In order to build this model, DSaPP is working with KIPP: Chicago, KIPP: New Jersey, The NOBEL Network, and Perspectives Charter Schools. High-performing charter schools graduate students from low-income backgrounds at high rates. Many of these students begin college, but most never complete their degrees. In order to improve their alumni’s college completion, many charter networks have alumni counselors who provide support through college in the form of academic and financial advising. However, given the limited time and resources of the alumni counselors, they would benefit from prioritizing their service by predicting risk levels and intervening appropriately.

The challenges identified here is similar to the challenges identified in predicting which students are at risk of dropping out of high school, enabling us to use a similar approach as described in that project. The initial findings of this project are available here.

Contact Information

University of Chicago
5730 S Ellis Ave #269
Chicago, IL 60637

datascience at uchicago dot edu

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